The proliferation of digital image photography, printing and image generation demands improved image processing techniques. These image processing techniques improve the perceived quality of images by manipulating the data captured and recorded by cameras and other devices. Lower cost devices can produce higher quality images through sophisticated image processing techniques performed on computers and peripheral devices. This satisfies the consumer's need for better quality images without spending large amounts of money for professional or even “prosumer” type devices.
One image processing technique called image-sharpening tends to increase the perceptibility of details in an image. Typically, image-sharpening operates by increasing pixel contrast on and around perceived edges in an image. If the edges are important to the image, this increases the visible details in the image and overall perceived quality of the image. Unfortunately, artifacts, noise and other details may not be desired yet will also be enhanced by image-sharpening operations. These sharpening operations can often make the image look “noisy” and appear of lower quality than if otherwise left alone.
Alternative image processing operations for smoothing operate to reduce or eliminate artifacts, noise and other undesired detailed elements of an image. Filters and other operations are applied to these images to soften or eliminate details perceived to be artifacts and noise. Smoothing preferably eliminates unwanted noise and artifacts by making neighboring pixels more consistent with each other. Applied indiscriminately, however, these smoothing filters have the deleterious effect of also eliminating desired details important to the image and can result in fuzzy or blurred images.
Active suppression of noise and artifacts during image processing is another method of improving image quality through image processing. These operations also have a smoothing effect primarily on or around sharp edges in an image. While these suppression methods may be more accurate, they can be computationally inefficient and therefore not cost effective to implement on lower cost hardware and software platforms.
Moreover, even high quality image processing methods cannot be applied successfully to all types of images. An image processing method that improves one image may be inappropriate when applied to another image. Further, one image processing technique may counteract the advantageous effects of another image processing technique.